Ferdinand Schlatt
PhD Student, efficient and effective neural IR models 🧠🔎
- Reposted by Ferdinand SchlattHappy to share that our paper "The Viability of Crowdsourcing for RAG Evaluation" received the Best Paper Honourable Mention at #SIGIR2025! Very grateful to the community for recognizing our work on improving RAG evaluation. 📄 webis.de/publications...
- Want to know how to make bi-encoders more than 3x faster with a new backbone encoder model? Check out our talk on the Token-Independent Text Encoder (TITE) #SIGIR2025 in the efficiency track. It pools vectors within the model to improve efficiency dl.acm.org/doi/10.1145/...
- @mrparryparry.bsky.social presenting our work on reproducing TREC DL 2019 judgements and the implications for evaluating modern ranking models on modern collections. Paper: arxiv.org/abs/2502.20937
- Thank you Carlos for the shout-out of Lightning IR in the LSR tutorial at #SIGIR2025 If you want to fine your own LSR models, check out our framework at github.com/webis-de/lig...
- Honored to receive the best short paper award and best paper honourable mention award at #ECIR2025. Thank you to all co-authors @maik-froebe.bsky.social, @hscells.bsky.social, Shengyao Zhuang, @bevankoopman.bsky.social, Guido Zuccon, Benno Stein, @martin-potthast.com, @matthias-hagen.bsky.social 🥳
- Short Paper: Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking webis.de/publications... Full Paper: Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders webis.de/publications...
